MMR-and-RMR-workflow-Clutch133

Load libraries (packages)

library("respR") ## respirometry/slope analysis
library("tidyverse") ## data manipulation

Set working directory

setwd("[PATH TO DIRECTORY]")

System1 - Dell

Importing data from firesting for resting

preexperiment_date <- "01 July 2023 11 15AM/All"
postexperiment_date <- "01 July 2023 03 42PM/All"

##--- last fish run in trial ---##
experiment_date <- "01 July 2023 12 24PM/Oxygen"
experiment_date2 <- "01 July 2023 12 24PM/All"

firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date2,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

System2 - Asus

Importing data from firesting for resting

preexperiment_date_asus <- "01 July 2023 11 35AM/All"
postexperiment_date_asus <- "01 July 2023 05 12PM/All"

##--- last fish run in trial ---##
experiment_date_asus <- "01 July 2023 01 26PM/Oxygen"
experiment_date2_asus <- "01 July 2023 01 26PM/All"

firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19)

Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

Chamber volumes

chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860 

chamber1_asus = 0.04565
chamber2_asus = 0.04573
chamber3_asus = 0.04551
chamber4_asus = 0.04791

Replicates

1

Enter specimen data

Date_tested="2023-07-01"
Clutch = "133" 
Replicate = 1 
Male = "CARL335" 
Female = "CARL59"
Population = "Arlington reef"
Tank =139 
mass = 0.0007814 
chamber = "ch4" 
Swim = "good/good"
salinity = 35 
chamber_vol = chamber4_dell
system1 = "Dell_7440"
Date_analysed = Sys.Date() 
Notes=""

Importing data from firesting for resting

##--- time of trail ---## 
experiment_mmr_date <- "01 July 2023 11 56AM/Oxygen"
experiment_mmr_date2 <- "01 July 2023 11 56AM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0005866958

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002789667

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   1   2   3   7   8  11  12  13  14  15  45  46 122 124 127 129 133 134 154
## [20] 155
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:   7    1     297.3911 -0.02646421 0.994      NA  3276   3531  7549.88
## 2:   9    1     324.4495 -0.02624352 0.991      NA  4348   4603  8630.11
## 3:  11    1     344.2096 -0.02535637 0.983      NA  5428   5683  9710.11
## 4:  15    1     405.3083 -0.02588780 0.992      NA  7588   7843 11870.11
## 5:  19    1     469.2989 -0.02650157 0.993      NA  9706   9961 14030.05
## 6:  21    1     504.6634 -0.02692436 0.994      NA 10786  11041 15110.05
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  7804.90 97.231 90.855 -0.02646421 -0.0004593619   -0.02600485 -0.02600485
## 2:  8885.11 97.503 90.941 -0.02624352 -0.0007897255   -0.02545380 -0.02545380
## 3:  9965.11 97.964 90.988 -0.02535637 -0.0011200218   -0.02423635 -0.02423635
## 4: 12125.11 97.686 91.239 -0.02588780 -0.0017806145   -0.02410718 -0.02410718
## 5: 14285.05 97.243 90.662 -0.02650157 -0.0024411888   -0.02406038 -0.02406038
## 6: 15365.05 97.642 90.737 -0.02692436 -0.0027714851   -0.02415288 -0.02415288
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.2898283
## 2:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.2836868
## 3:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.2701181
## 4:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.2686785
## 5:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.2681569
## 6:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.2691878
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -370.9090          NA  mgO2/hr/kg   -370.9090
## 2:   -363.0494          NA  mgO2/hr/kg   -363.0494
## 3:   -345.6848          NA  mgO2/hr/kg   -345.6848
## 4:   -343.8424          NA  mgO2/hr/kg   -343.8424
## 5:   -343.1749          NA  mgO2/hr/kg   -343.1749
## 6:   -344.4942          NA  mgO2/hr/kg   -344.4942
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
133	1	CARL335	CARL59	Arlington reef	139	0.0007814	ch4	Dell_7440	0.0486	2023-07-01	2024-06-07	good/good	35	28.5	353.596	0.2762999	0.9908

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.54
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch4
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  6  7  8  9 10 11 12 13 15 16 18 19 22 23 24 25 26
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.09
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     245.5802 -0.05528926 0.9987292      NA 189    250 2778.66
##   2:  NA    2     245.5498 -0.05527943 0.9986711      NA 194    255 2783.66
##   3:  NA    3     245.4870 -0.05525632 0.9987134      NA 188    249 2777.66
##   4:  NA    4     245.4518 -0.05524426 0.9986905      NA 193    254 2782.66
##   5:  NA    5     245.4510 -0.05524368 0.9987091      NA 187    248 2776.66
##  ---                                                                       
## 236:  NA  236     193.6761 -0.03632275 0.9724892      NA   5     66 2594.66
## 237:  NA  237     191.8981 -0.03564622 0.9687555      NA   4     65 2593.66
## 238:  NA  238     190.2929 -0.03503531 0.9652987      NA   3     64 2592.66
## 239:  NA  239     188.8415 -0.03448258 0.9624886      NA   2     63 2591.66
## 240:  NA  240     187.5552 -0.03399265 0.9597644      NA   1     62 2590.66
##      endtime    oxy endoxy        rate
##   1: 2838.66 91.974 88.643 -0.05528926
##   2: 2843.66 91.743 88.316 -0.05527943
##   3: 2837.66 92.041 88.698 -0.05525632
##   4: 2842.66 91.803 88.369 -0.05524426
##   5: 2836.66 92.041 88.717 -0.05524368
##  ---                                  
## 236: 2654.66 99.158 97.134 -0.03632275
## 237: 2653.66 99.196 97.203 -0.03564622
## 238: 2652.66 99.246 97.254 -0.03503531
## 239: 2651.66 99.278 97.326 -0.03448258
## 240: 2650.66 99.212 97.340 -0.03399265
## 
## Regressions : 240 | Results : 240 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 240 adjusted rate(s):
## Rate          : -0.05528926
## Adjustment    : 0.0005866958
## Adjusted Rate : -0.05587596 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.89,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.89 and 1...

## ----- Selection complete. 0 rate(s) removed, 240 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 239 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     245.5802 -0.05528926 0.9987292      NA 189    250 2778.66
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 2838.66 91.974 88.643 -0.05528926 0.0005866958   -0.05587596 -0.05587596
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0486 0.0007814   NA 35 28.5 1.013253 -0.6227467
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -796.9628          NA  mgO2/hr/kg   -796.9628
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
133	1	CARL335	CARL59	Arlington reef	139	0.0007814	ch4	Dell_7440	0.0486	2023-07-01	2024-06-07	good/good	35	28.5	353.596	0.2762999	0.9908	796.9628	0.6227467	0.9987292	443.3668	0.3464469

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 12 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

2

Enter specimen data

Date_tested="2023-07-01"
Clutch = "133" 
Replicate = 2 
Male = "CARL335" 
Female = "CARL59"
Population = "Arlington reef"
Tank =139 
mass = 0.0006414 
chamber = "ch3" 
Swim = "good/good"
salinity = 35 
chamber_vol = chamber3_dell
system1 = "Dell_7440"
Date_analysed = Sys.Date() 
Notes="max file missing!"

Importing data from firesting for resting

##--- time of trail ---## 
experiment_mmr_date <- "01 July 2023 12 13PM/Oxygen"
experiment_mmr_date2 <- "01 July 2023 12 13PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0007788385

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.002025236

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   1   2   3   7   8  11  12  13  14  15  45  46 122 124 127 129 133 134 154
## [20] 155
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   3    1     183.8321 -0.01575618 0.979      NA 1118   1373  5389.72
## 2:   4    1     185.7504 -0.01460944 0.964      NA 1656   1911  5929.88
## 3:   5    1     214.3886 -0.01775390 0.963      NA 2196   2451  6469.89
## 4:  10    1     246.9046 -0.01626339 0.978      NA 4888   5143  9170.11
## 5:  13    1     305.4823 -0.01921518 0.978      NA 6508   6763 10790.11
## 6:  14    1     303.2145 -0.01810185 0.989      NA 7048   7303 11330.11
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  5644.72 98.556 95.025 -0.01575618  0.0004587523   -0.01621493 -0.01621493
## 2:  6184.88 98.688 95.111 -0.01460944  0.0003215559   -0.01493100 -0.01493100
## 3:  6724.88 98.910 94.906 -0.01775390  0.0001843988   -0.01793830 -0.01793830
## 4:  9425.11 98.042 93.708 -0.01626339 -0.0005014374   -0.01576196 -0.01576196
## 5: 11045.11 98.174 93.157 -0.01921518 -0.0009129049   -0.01830228 -0.01830228
## 6: 11585.11 98.169 93.439 -0.01810185 -0.0010500607   -0.01705179 -0.01705179
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.1850687
## 2:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.1704145
## 3:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.2047383
## 4:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.1798987
## 5:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.2088925
## 6:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.1946202
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -288.5387          NA  mgO2/hr/kg   -288.5387
## 2:   -265.6915          NA  mgO2/hr/kg   -265.6915
## 3:   -319.2054          NA  mgO2/hr/kg   -319.2054
## 4:   -280.4781          NA  mgO2/hr/kg   -280.4781
## 5:   -325.6821          NA  mgO2/hr/kg   -325.6821
## 6:   -303.4303          NA  mgO2/hr/kg   -303.4303
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
133	2	CARL335	CARL59	Arlington reef	139	0.0006414	ch3	Dell_7440	0.04977	2023-07-01	2024-06-07	good/good	35	28.5	303.4669	0.1946437	0.9774

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 1.66
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   8  11  12  13  14  15  81  82  83  88  89  90  91  94  95  96  97  98  99
## [20] 106
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 1.11
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0     slope_b1       rsq density row endrow    time
##   1:  NA    1     272.3235 -0.046098613 0.9809669      NA 175    235 3805.04
##   2:  NA    2     270.8268 -0.045711865 0.9802460      NA 174    234 3804.03
##   3:  NA    3     270.5100 -0.045631005 0.9796725      NA 173    233 3803.03
##   4:  NA    4     270.4169 -0.045600242 0.9792223      NA 176    236 3806.03
##   5:  NA    5     269.9794 -0.045494355 0.9786826      NA 172    232 3802.03
##  ---                                                                        
## 237:  NA  237     125.0906 -0.007186836 0.8213379      NA  41    101 3671.03
## 238:  NA  238     125.0396 -0.007173094 0.8208125      NA  42    102 3672.03
## 239:  NA  239     124.7520 -0.007096103 0.8161405      NA  45    105 3675.03
## 240:  NA  240     124.7112 -0.007084408 0.8172797      NA  43    103 3673.03
## 241:  NA  241     124.5730 -0.007047293 0.8164350      NA  44    104 3674.03
##      endtime    oxy endoxy         rate
##   1: 3865.04 96.656 94.443 -0.046098613
##   2: 3864.03 96.669 94.374 -0.045711865
##   3: 3863.03 96.747 94.402 -0.045631005
##   4: 3866.03 96.707 94.419 -0.045600242
##   5: 3862.03 96.762 94.382 -0.045494355
##  ---                                   
## 237: 3731.03 98.715 98.274 -0.007186836
## 238: 3732.03 98.728 98.270 -0.007173094
## 239: 3735.03 98.716 98.179 -0.007096103
## 240: 3733.03 98.723 98.289 -0.007084408
## 241: 3734.03 98.721 98.247 -0.007047293
## 
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 241 adjusted rate(s):
## Rate          : -0.04609861
## Adjustment    : 0.0007788385
## Adjusted Rate : -0.04687745 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.89,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.89 and 1...

## ----- Selection complete. 77 rate(s) removed, 164 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 163 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     272.3235 -0.04609861 0.9809669      NA 175    235 3805.04
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 3865.04 96.656 94.443 -0.04609861 0.0007788385   -0.04687745 -0.04687745
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04977 0.0006414   NA 35 28.5 1.013253 -0.5350345
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -834.1667          NA  mgO2/hr/kg   -834.1667
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
133	2	CARL335	CARL59	Arlington reef	139	0.0006414	ch3	Dell_7440	0.04977	2023-07-01	2024-06-07	good/good	35	28.5	303.4669	0.1946437	0.9774	834.1667	0.5350345	0.9809669	530.6998	0.3403909	max file missing!

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 13 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

3

Enter specimen data

Date_tested="2023-07-01"
Clutch = "133" 
Replicate = 3 
Male = "CARL335" 
Female = "CARL59"
Population = "Arlington reef"
Tank =139 
mass = 0.0007225 
chamber = "ch2" 
Swim = "good/good"
salinity = 35 
chamber_vol = chamber2_dell
system1 = "Dell_7440"
Date_analysed = Sys.Date() 
Notes="cycle2 used for max"

Importing data from firesting for resting

##--- time of trail ---## 
experiment_mmr_date <- "01 July 2023 12 24PM/Oxygen"
experiment_mmr_date2 <- "01 July 2023 12 24PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.001129758

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.0008588053

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   1   2   3   7   8  11  12  13  14  15  45  46 122 124 127 129 133 134 154
## [20] 155
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:  16    1     461.5410 -0.02923345 0.991      NA  8128   8383 12410.11
## 2:  17    1     495.1515 -0.03058216 0.993      NA  8668   8923 12950.12
## 3:  18    1     496.9566 -0.02952503 0.994      NA  9183   9428 13490.16
## 4:  19    1     504.6580 -0.02896548 0.991      NA  9706   9961 14030.05
## 5:  20    1     524.2505 -0.02922910 0.981      NA 10246  10501 14570.05
## 6:  21    1     549.9433 -0.02987925 0.990      NA 10786  11041 15110.05
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1: 12665.11 98.543 91.122 -0.02923345 -0.0003617744   -0.02887168 -0.02887168
## 2: 13205.11 98.801 91.420 -0.03058216 -0.0004590420   -0.03012312 -0.03012312
## 3: 13744.33 98.511 90.836 -0.02952503 -0.0005562420   -0.02896879 -0.02896879
## 4: 14285.05 98.048 90.976 -0.02896548 -0.0006535636   -0.02831192 -0.02831192
## 5: 14825.05 97.936 90.438 -0.02922910 -0.0007508303   -0.02847827 -0.02847827
## 6: 15365.05 98.442 91.028 -0.02987925 -0.0008480970   -0.02903116 -0.02903116
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.3041016
## 2:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.3172828
## 3:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.3051244
## 4:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.2982057
## 5:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.2999579
## 6:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.3057813
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -420.9018          NA  mgO2/hr/kg   -420.9018
## 2:   -439.1458          NA  mgO2/hr/kg   -439.1458
## 3:   -422.3175          NA  mgO2/hr/kg   -422.3175
## 4:   -412.7415          NA  mgO2/hr/kg   -412.7415
## 5:   -415.1666          NA  mgO2/hr/kg   -415.1666
## 6:   -423.2267          NA  mgO2/hr/kg   -423.2267
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
133	3	CARL335	CARL59	Arlington reef	139	0.0007225	ch2	Dell_7440	0.04593	2023-07-01	2024-06-07	good/good	35	28.5	424.1517	0.3064496	0.9898

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch2
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   1   2   3   7   8  11  12  13  14  15  45  46 122 124 127 129 133 134 154
## [20] 155
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.22
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     426.9674 -0.07649365 0.9854626      NA  69    129 4333.04
##   2:  NA    2     426.2708 -0.07633042 0.9864668      NA  71    131 4335.03
##   3:  NA    3     426.0015 -0.07627409 0.9847937      NA  68    128 4332.04
##   4:  NA    4     425.7134 -0.07620042 0.9859804      NA  72    132 4336.03
##   5:  NA    5     425.3514 -0.07612248 0.9855683      NA  70    130 4334.03
##  ---                                                                       
## 237:  NA  237     196.2558 -0.02448039 0.8159801      NA 222    282 4486.03
## 238:  NA  238     195.1608 -0.02425003 0.8187516      NA 226    286 4490.03
## 239:  NA  239     194.6683 -0.02413172 0.8217532      NA 223    283 4487.03
## 240:  NA  240     194.0212 -0.02399464 0.8244130      NA 225    285 4489.03
## 241:  NA  241     190.7738 -0.02327439 0.8209671      NA 224    284 4488.05
##      endtime    oxy endoxy        rate
##   1: 4393.04 95.205 91.101 -0.07649365
##   2: 4395.03 95.111 91.090 -0.07633042
##   3: 4392.04 95.371 91.079 -0.07627409
##   4: 4396.03 95.056 91.076 -0.07620042
##   5: 4394.03 95.018 91.086 -0.07612248
##  ---                                  
## 237: 4546.03 86.920 84.755 -0.02448039
## 238: 4550.03 86.691 84.305 -0.02425003
## 239: 4547.03 86.812 84.662 -0.02413172
## 240: 4549.03 86.675 84.444 -0.02399464
## 241: 4548.05 86.798 84.538 -0.02327439
## 
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 241 adjusted rate(s):
## Rate          : -0.07649365
## Adjustment    : 0.001129758
## Adjusted Rate : -0.0776234 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.89,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.89 and 1...

## ----- Selection complete. 29 rate(s) removed, 212 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 211 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     426.9674 -0.07649365 0.9854626      NA  69    129 4333.04
##    endtime    oxy endoxy        rate  adjustment rate.adjusted rate.input
## 1: 4393.04 95.205 91.101 -0.07649365 0.001129758    -0.0776234 -0.0776234
##    oxy.unit time.unit  volume      mass area  S    t        P  rate.abs
## 1:     %Air       sec 0.04593 0.0007225   NA 35 28.5 1.013253 -0.817597
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1131.622          NA  mgO2/hr/kg   -1131.622
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
133	3	CARL335	CARL59	Arlington reef	139	0.0007225	ch2	Dell_7440	0.04593	2023-07-01	2024-06-07	good/good	35	28.5	424.1517	0.3064496	0.9898	1131.622	0.817597	0.9854626	707.4705	0.5111474	cycle2 used for max

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 14 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

4

Enter specimen data

Date_tested="2023-07-01"
Clutch = "133" 
Replicate = 4 
Male = "CARL335" 
Female = "CARL59"
Population = "Arlington reef"
Tank =139 
mass = 0.0007161 
chamber = "ch1" 
Swim = "good/good"
salinity = 35 
chamber_vol = chamber1_dell
system1 = "Dell_7440"
Date_analysed = Sys.Date() 
Notes=""

Importing data from firesting for resting

##--- time of trail ---## 
experiment_mmr_date <- "01 July 2023 12 24PM/Oxygen"
experiment_mmr_date2 <- "01 July 2023 12 24PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.001331357

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.003655077

Resting metabolic rate

Data manipulation

firesting2 <- firesting |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME) 
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME) 
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])  

apoly_insp <- firesting2 |> 
  subset_data(from=Tstart.dTIME, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   1   2   3   7   8  11  12  13  14  15  45  46 122 124 127 129 133 134 154
## [20] 155
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=255, 
                              by="time", 
                              plot=TRUE)  

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.

## -----------------------------------------

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates... 
## To plot others modify 'pos' input.

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density   row endrow     time
## 1:  14    1     428.0311 -0.02902446 0.983      NA  7048   7303 11330.11
## 2:  16    1     449.3880 -0.02823141 0.991      NA  8128   8383 12410.11
## 3:  17    1     461.3694 -0.02797077 0.985      NA  8668   8923 12950.12
## 4:  19    1     525.6071 -0.03040348 0.987      NA  9706   9961 14030.05
## 5:  20    1     481.9214 -0.02629407 0.984      NA 10246  10501 14570.05
## 6:  21    1     537.2852 -0.02900346 0.978      NA 10786  11041 15110.05
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 11585.11 98.942 91.428 -0.02902446 -0.001920941   -0.02710352 -0.02710352
## 2: 12665.11 98.725 91.571 -0.02823141 -0.002408744   -0.02582266 -0.02582266
## 3: 13205.11 98.799 91.956 -0.02797077 -0.002652648   -0.02531812 -0.02531812
## 4: 14285.05 98.759 90.697 -0.03040348 -0.003140422   -0.02726305 -0.02726305
## 5: 14825.05 98.392 92.134 -0.02629407 -0.003384324   -0.02290975 -0.02290975
## 6: 15365.05 98.921 91.817 -0.02900346 -0.003628226   -0.02537523 -0.02537523
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.2890207
## 2:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.2753621
## 3:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.2699818
## 4:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.2907218
## 5:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.2443000
## 6:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.2705909
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -403.6038          NA  mgO2/hr/kg   -403.6038
## 2:   -384.5302          NA  mgO2/hr/kg   -384.5302
## 3:   -377.0170          NA  mgO2/hr/kg   -377.0170
## 4:   -405.9794          NA  mgO2/hr/kg   -405.9794
## 5:   -341.1534          NA  mgO2/hr/kg   -341.1534
## 6:   -377.8675          NA  mgO2/hr/kg   -377.8675
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple")  

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
133	4	CARL335	CARL59	Arlington reef	139	0.0007161	ch1	Dell_7440	0.0465	2023-07-01	2024-06-07	good/good	35	28.5	389.7996	0.2791355	0.9848

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 15 16 17 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.94 7.77
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]   1   2   3   7   8  11  12  13  14  15  45  46 122 124 127 129 133 134 154
## [20] 155
## Minimum and Maximum intervals in uneven Time data: 
## [1] 0.95 1.22
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     429.8323 -0.07703628 0.9356321      NA  55    115 4319.03
##   2:  NA    2     429.6066 -0.07698392 0.9355799      NA  54    114 4318.03
##   3:  NA    3     428.7239 -0.07677938 0.9356559      NA  53    113 4317.03
##   4:  NA    4     428.4330 -0.07671507 0.9357787      NA  51    111 4315.03
##   5:  NA    5     427.9905 -0.07661100 0.9353134      NA  52    112 4316.03
##  ---                                                                       
## 237:  NA  237     286.0251 -0.04378749 0.8983744      NA  12     72 4276.03
## 238:  NA  238     285.6386 -0.04369945 0.8985708      NA  13     73 4277.03
## 239:  NA  239     285.5407 -0.04367931 0.8983460      NA  15     75 4279.03
## 240:  NA  240     285.3887 -0.04364551 0.8984006      NA  16     76 4280.03
## 241:  NA  241     284.5227 -0.04344158 0.8986171      NA  14     74 4278.03
##      endtime    oxy endoxy        rate
##   1: 4379.03 97.328 92.415 -0.07703628
##   2: 4378.03 97.230 92.312 -0.07698392
##   3: 4377.03 97.401 92.655 -0.07677938
##   4: 4375.03 97.144 93.323 -0.07671507
##   5: 4376.03 97.239 93.061 -0.07661100
##  ---                                  
## 237: 4336.03 99.031 96.056 -0.04378749
## 238: 4337.03 98.963 95.920 -0.04369945
## 239: 4339.03 98.835 95.757 -0.04367931
## 240: 4340.03 98.313 95.785 -0.04364551
## 241: 4338.03 98.796 95.995 -0.04344158
## 
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 241 adjusted rate(s):
## Rate          : -0.07703628
## Adjustment    : 0.001331357
## Adjusted Rate : -0.07836763 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.89,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.89 and 1...

## ----- Selection complete. 14 rate(s) removed, 227 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 226 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     429.8323 -0.07703628 0.9356321      NA  55    115 4319.03
##    endtime    oxy endoxy        rate  adjustment rate.adjusted  rate.input
## 1: 4379.03 97.328 92.415 -0.07703628 0.001331357   -0.07836763 -0.07836763
##    oxy.unit time.unit volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.0465 0.0007161   NA 35 28.5 1.013253 -0.8356798
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1166.988          NA  mgO2/hr/kg   -1166.988
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass, 
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
                      Notes=Notes, 
                      True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
133	4	CARL335	CARL59	Arlington reef	139	0.0007161	ch1	Dell_7440	0.0465	2023-07-01	2024-06-07	good/good	35	28.5	389.7996	0.2791355	0.9848	1166.988	0.8356798	0.9356321	777.1879	0.5565443

Exporting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 15 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

6

Enter specimen data

Date_tested="2023-07-01"
Clutch = "133" 
Replicate = 6 
Male = "CARL335" 
Female = "CARL59"
Population = "Arlington reef"
Tank =139 
mass = 0.0009275 
chamber = "ch3" 
Swim = "good/good"
salinity = 35 
chamber_vol = chamber1_asus 
system1 = "Asus"
Date_analysed = Sys.Date() 
Notes="Chamber1 was accidentally in the position of chamber 3"

Importing data from firesting for resting

##--- time of trail ---## 
experiment_mmr_date_asus <- "01 July 2023 12 59PM/Oxygen"
experiment_mmr_date2_asus <- "01 July 2023 12 59PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0002751484

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_4.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_5.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.001193798

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.31 3.91
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=8900, 
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  6  7  8  9 10 13 14 17 18 19 20 21 22 23 24 25
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.31 3.91
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, 
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 17 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 11 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:   1    1     385.7352 -0.03229453 0.989      NA   35    217  8945.25
## 2:   2    1     398.0138 -0.03171553 0.985      NA  437    620  9484.74
## 3:   3    1     441.4587 -0.03436423 0.987      NA  840   1022 10025.15
## 4:   4    1     476.1943 -0.03588540 0.994      NA 1242   1425 10564.50
## 5:  14    1     702.4366 -0.03793217 0.996      NA 5179   5362 15963.69
## 6:  15    1     728.7506 -0.03825586 0.995      NA 5581   5763 16504.27
##     endtime    oxy endoxy        rate    adjustment rate.adjusted  rate.input
## 1:  9189.47 96.713 88.400 -0.03229453 -3.510756e-05   -0.03225943 -0.03225943
## 2:  9730.06 97.000 88.691 -0.03171553 -1.869624e-04   -0.03152857 -0.03152857
## 3: 10269.26 96.653 88.158 -0.03436423 -3.387512e-04   -0.03402548 -0.03402548
## 4: 10810.03 96.670 87.887 -0.03588540 -4.906117e-04   -0.03539479 -0.03539479
## 5: 16209.74 96.714 87.535 -0.03793217 -2.008893e-03   -0.03592328 -0.03592328
## 6: 16749.32 97.026 88.046 -0.03825586 -2.160760e-03   -0.03609510 -0.03609510
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.3377129
## 2:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.3300617
## 3:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.3562011
## 4:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.3705359
## 5:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.3760685
## 6:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.3778673
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -364.1109          NA  mgO2/hr/kg   -364.1109
## 2:   -355.8617          NA  mgO2/hr/kg   -355.8617
## 3:   -384.0443          NA  mgO2/hr/kg   -384.0443
## 4:   -399.4996          NA  mgO2/hr/kg   -399.4996
## 5:   -405.4647          NA  mgO2/hr/kg   -405.4647
## 6:   -407.4041          NA  mgO2/hr/kg   -407.4041
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
133	6	CARL335	CARL59	Arlington reef	139	0.0009275	ch3	Asus	0.04565	2023-07-01	2024-06-07	good/good	35	28.5	392.1047	0.3636771	0.9922

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.53
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch3
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 11 12 13 15 16 17 18 19 20 21 22
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.41
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     548.8235 -0.08697971 0.9890049      NA  62    107 5228.63
##   2:  NA    2     548.2229 -0.08686387 0.9887823      NA  63    108 5230.00
##   3:  NA    3     547.9965 -0.08682415 0.9886958      NA  61    106 5227.29
##   4:  NA    4     546.4815 -0.08653837 0.9879271      NA  60    105 5225.97
##   5:  NA    5     546.4207 -0.08651974 0.9881384      NA  64    109 5231.36
##  ---                                                                       
## 175:  NA  175     329.5415 -0.04507106 0.9728835      NA  24     69 5177.64
## 176:  NA  176     327.5680 -0.04469830 0.9718853      NA  28     73 5182.99
## 177:  NA  177     327.0229 -0.04458842 0.9738854      NA  25     70 5178.96
## 178:  NA  178     325.3092 -0.04426266 0.9751580      NA  27     72 5181.64
## 179:  NA  179     324.9817 -0.04419807 0.9755215      NA  26     71 5180.29
##      endtime    oxy endoxy        rate
##   1: 5288.63 93.829 88.986 -0.08697971
##   2: 5290.00 93.790 88.929 -0.08686387
##   3: 5287.29 93.820 89.148 -0.08682415
##   4: 5285.97 93.951 89.278 -0.08653837
##   5: 5291.36 93.734 88.870 -0.08651974
##  ---                                  
## 175: 5237.64 96.440 93.554 -0.04507106
## 176: 5242.99 96.089 92.856 -0.04469830
## 177: 5238.96 96.345 93.426 -0.04458842
## 178: 5241.64 96.168 93.086 -0.04426266
## 179: 5240.29 96.307 93.290 -0.04419807
## 
## Regressions : 179 | Results : 179 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 179 adjusted rate(s):
## Rate          : -0.08697971
## Adjustment    : 0.0002751484
## Adjusted Rate : -0.08725486 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.89,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.89 and 1...

## ----- Selection complete. 0 rate(s) removed, 179 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 178 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     548.8235 -0.08697971 0.9890049      NA  62    107 5228.63
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 5288.63 93.829 88.986 -0.08697971 0.0002751484   -0.08725486 -0.08725486
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04565 0.0009275   NA 35 28.5 1.013253 -0.9134412
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -984.8423          NA  mgO2/hr/kg   -984.8423
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
133	6	CARL335	CARL59	Arlington reef	139	0.0009275	ch3	Asus	0.04565	2023-07-01	2024-06-07	good/good	35	28.5	392.1047	0.3636771	0.9922	984.8423	0.9134412	0.9890049	592.7376	0.5497641	Chamber1 was accidentally in the position of chamber 3
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 16 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)

8

Enter specimen data

Date_tested="2023-07-01"
Clutch = "133" 
Replicate = 8 
Male = "CARL335" 
Female = "CARL59"
Population = "Arlington reef"
Tank =139 
mass = 0.0008797 
chamber = "ch1" 
Swim = "good/good"
salinity = 35 
chamber_vol = chamber2_asus 
system1 = "Asus"
Date_analysed = Sys.Date() 
Notes="Chamber2 was accidentally in the position of chamber1"

Importing data from firesting for resting

##--- time of trail ---## 
experiment_mmr_date_asus <- "01 July 2023 01 16PM/Oxygen"
experiment_mmr_date2_asus <- "01 July 2023 01 16PM/All"

firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"), 
    delim = "\t", escape_double = FALSE, 
    col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"), 
        `Time (s)` = col_number(), Ch1...5 = col_number(), 
        Ch2...6 = col_number(), Ch3...7 = col_number(), 
        Ch4...8 = col_number()), trim_ws = TRUE, 
    skip = 19) 

## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`

## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)

Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"), 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        `Seconds from start for linreg` = col_number(), 
        `ch1 po2` = col_number(), `ch2 po2` = col_number(), 
        `ch3 po2` = col_number(), `ch4 po2` = col_number(), 
        ...8 = col_skip()), trim_ws = TRUE) 

## New names:
## • `` -> `...8`

Background rates

Pre-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes")) 

pre_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

pre_cycle2 <- read_delim("./Cycle_2.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

pre_cycle3 <- read_delim("./Cycle_3.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))


bg_pre1 <- pre_cycle1 %>% calc_rate.bg()

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre2 <- pre_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre3 <- pre_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_pre <- mean(bg_pre1$rate.bg.mean,bg_pre2$rate.bg.mean,bg_pre3$rate.bg.mean) 
bg_pre

## [1] 0.0001987835

post-experiment

setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes")) 
 

post_cycle1 <- read_delim("./Cycle_1.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

post_cycle2 <- read_delim("./Cycle_4.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber)) 

post_cycle3 <- read_delim("./Cycle_5.txt", 
    delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"), 
        ...8 = col_skip()), trim_ws = TRUE) %>% 
  rename(dTIME = `Seconds from start for linreg`, 
         ch1 =`ch1 po2`, 
         ch2 =`ch2 po2`, 
         ch3 =`ch3 po2`, 
         ch4 =`ch4 po2`) %>% 
  select(c("Time",chamber))

bg_post1 <- post_cycle1 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post2 <- post_cycle2 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post3 <- post_cycle3 %>% calc_rate.bg() 

## 
## # plot.calc_rate.bg # -------------------

## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------

bg_post <- mean(bg_post1$rate.bg.mean,bg_post2$rate.bg.mean,bg_post3$rate.bg.mean)
bg_post 

## [1] -0.004254762

Resting metabolic rate

Data manipulation

firesting2_asus <- firesting_asus |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_asus, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.31 3.91
## -----------------------------------------

#### subset data

Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME) 
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"]) 

Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME) 
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])  

apoly_insp <- firesting2_asus |> 
  subset_data(from=8900, # altered for sample
              to=Tend.dTIME, 
              by="time") 

apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  2  3  4  5  6  7  8  9 10 13 14 17 18 19 20 21 22 23 24 25
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.31 3.91
## -----------------------------------------

Extract rates

apoly_cr.int <- calc_rate.int(apoly_insp, 
                              starts=(195+45+300), 
                              wait=45, 
                              measure=245, # altered for sample
                              by="time", 
                              plot=TRUE) 

## 
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...

## -----------------------------------------

adjust rates for background

apoly_cr.int_adj <- adjust_rate(apoly_cr.int, 
                                by = bg_pre, 
                                by2 = bg_post, 
                                time_by = Tstart.row, 
                                time_by2 = Tend.row,
                                method = "linear")

## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates. 
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.

## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve). 
## Ensure this is correct. The 'linear' adjustment has been performed regardless.

## adjust_rate: Rate adjustments applied using "linear" method.

apoly_cr.int_adj$summary

Converting units

apoly_cr.int_adj2 <- apoly_cr.int_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253) 

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

apoly_cr.int_adj2$summary

Plot curve

ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) + 
  geom_point() + 
  stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
  theme_classic()

Rate filtering

apoly_rmr <- apoly_cr.int_adj2 |> 
  select_rate(method ="rsq", n=c(0.95,1)) |> 
  select_rate(method="lowest", n=6) |> 
  plot(type="full") |> 
  summary(export = TRUE)

## select_rate: Selecting rates with rsq values between 0.95 and 1...

## ----- Selection complete. 0 rate(s) removed, 17 rate(s) remaining -----

## select_rate: Selecting lowest 6 *absolute* rate values...

## ----- Selection complete. 11 rate(s) removed, 6 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1   rsq density  row endrow     time
## 1:  10    1     582.2060 -0.03524067 0.992      NA 3571   3753 13805.97
## 2:  11    1     593.2404 -0.03472165 0.983      NA 3972   4155 14343.59
## 3:  13    1     606.0406 -0.03316256 0.997      NA 4778   4960 15425.42
## 4:  14    1     666.8671 -0.03581391 0.998      NA 5179   5362 15963.69
## 5:  16    1     721.0309 -0.03671438 0.997      NA 5982   6164 17044.03
## 6:  17    1     677.4309 -0.03308923 0.996      NA 6383   6565 17584.37
##     endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 14049.86 95.576 87.492 -0.03524067 -0.004657358   -0.03058332 -0.03058332
## 2: 14589.18 95.657 87.155 -0.03472165 -0.004950345   -0.02977131 -0.02977131
## 3: 15669.66 94.755 86.018 -0.03316256 -0.005538613   -0.02762395 -0.02762395
## 4: 16209.74 95.364 86.186 -0.03581391 -0.005831984   -0.02998193 -0.02998193
## 5: 17289.21 95.399 86.487 -0.03671438 -0.006419571   -0.03029481 -0.03029481
## 6: 17829.70 95.687 87.312 -0.03308923 -0.006713617   -0.02637561 -0.02637561
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.3207273
## 2:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.3122118
## 3:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.2896924
## 4:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.3144206
## 5:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.3177018
## 6:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.2766011
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -364.5872          NA  mgO2/hr/kg   -364.5872
## 2:   -354.9071          NA  mgO2/hr/kg   -354.9071
## 3:   -329.3082          NA  mgO2/hr/kg   -329.3082
## 4:   -357.4179          NA  mgO2/hr/kg   -357.4179
## 5:   -361.1478          NA  mgO2/hr/kg   -361.1478
## 6:   -314.4266          NA  mgO2/hr/kg   -314.4266
## -----------------------------------------

## remove lowest slope 
apoly_rmr <- apoly_rmr |> 
  filter(rate.output != max(rate.output))

Results

results <- data.frame(Clutch = Clutch, 
                      Replicate =Replicate, 
                      Male=Male, 
                      Female=Female,
                      Population = Population, 
                      Tank = Tank,
                      Mass = mass, 
                      Chamber = chamber, 
                      System = system1,
                      Volume = chamber_vol, 
                      Date_tested = Date_tested, 
                      Date_analysed =Date_analysed,
                      Swim = Swim,
                      Salinity = salinity, 
                      Temperature = as.numeric(unique(firesting2$temperature)), 
                      Resting_kg = mean(apoly_rmr$rate.output*-1), 
                      Resting =  mean(apoly_rmr$rate.output*-1)*mass, 
                      rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest
133	8	CARL335	CARL59	Arlington reef	139	0.0008797	ch1	Asus	0.04573	2023-07-01	2024-06-07	good/good	35	28.5	353.4737	0.3109508	0.9934

Maximum oxygen consumption

Data manipulation

firesting2_mmr <- firesting_mmr |>
  select(c(1:3,5:9)) |> 
  rename(TIME = `Time (HH:MM:SS)`, 
         dTIME = `Time (s)`, 
         ch1 = Ch1...5, 
         ch2 = Ch2...6,
         ch3 = Ch3...7, 
         ch4 = Ch4...8, 
         temperature= `Ch 1...9`) |> 
  select(c("dTIME",all_of(chamber),"TIME","temperature"))

Inspect file

inspect(firesting2_mmr, time=1, oxygen=2)

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.54
## -----------------------------------------

Subset data

cycle1.start <-  Cycle_1.mmr[1,1]
cycle1.end <-  tail(Cycle_1.mmr, n=1)[1,1] 

cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start

## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1.end.row <- which(firesting2_mmr$TIME == cycle1.end); cycle1.end 

## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="

cycle1_data <- firesting2_mmr |> 
  subset_data(from = cycle1.start.row, 
              to = cycle1.end.row, 
              by = "row") 

## subset_data: Multi-column dataset detected in input! 
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively. 
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.

inspect(cycle1_data)

## inspect: Applying column default of 'time = 1'

## inspect: Applying column default of 'oxygen = 2'

## Warning: inspect: Time values are not evenly-spaced (numerically).

## inspect: Data issues detected. For more information use print().

## 
## # print.inspect # -----------------------
##                 dTIME  ch1
## numeric          pass pass
## Inf/-Inf         pass pass
## NA/NaN           pass pass
## sequential       pass    -
## duplicated       pass    -
## evenly-spaced    WARN    -
## 
## Uneven Time data locations (first 20 shown) in column: dTIME 
##  [1]  4  5  6  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
## Minimum and Maximum intervals in uneven Time data: 
## [1] 1.32 1.39
## -----------------------------------------

Calculating MMR

mmr <- auto_rate(cycle1_data, method = "highest", plot=TRUE, width=60, by="time") |> 
  summary()

## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
##   If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()

## 
## # summary.auto_rate # -------------------
## 
## === Summary of Results by Highest Rate ===
##      rep rank intercept_b0    slope_b1       rsq density row endrow    time
##   1:  NA    1     644.7807 -0.08914513 0.9955127      NA  38     83 6159.56
##   2:  NA    2     644.6765 -0.08912807 0.9955052      NA  39     84 6160.89
##   3:  NA    3     644.4706 -0.08909544 0.9954277      NA  34     79 6154.16
##   4:  NA    4     644.0490 -0.08902733 0.9954233      NA  35     80 6155.51
##   5:  NA    5     644.0434 -0.08902559 0.9954678      NA  40     85 6162.24
##  ---                                                                       
## 176:  NA  176     212.6470 -0.02038174 0.6939691      NA 158    203 6320.54
## 177:  NA  177     211.8359 -0.02025546 0.6932571      NA 159    204 6321.86
## 178:  NA  178     211.6503 -0.02022763 0.6930749      NA 160    205 6323.21
## 179:  NA  179     211.1553 -0.02015320 0.6925984      NA 162    207 6325.90
## 180:  NA  180     210.8288 -0.02009993 0.6926316      NA 161    206 6324.56
##      endtime    oxy endoxy        rate
##   1: 6219.56 95.615 90.380 -0.08914513
##   2: 6220.89 95.576 90.255 -0.08912807
##   3: 6214.16 96.109 90.793 -0.08909544
##   4: 6215.51 96.099 90.706 -0.08902733
##   5: 6222.24 95.473 90.157 -0.08902559
##  ---                                  
## 176: 6380.54 84.116 82.428 -0.02038174
## 177: 6381.86 84.028 82.397 -0.02025546
## 178: 6383.21 83.962 82.346 -0.02022763
## 179: 6385.90 83.941 82.181 -0.02015320
## 180: 6384.56 83.974 82.306 -0.02009993
## 
## Regressions : 180 | Results : 180 | Method : highest | Roll width : 60 | Roll type : time 
## -----------------------------------------

Adjusting

mmr_adj <- adjust_rate(mmr, by=bg_pre, method = "mean");mmr_adj

## adjust_rate: Rate adjustments applied using "mean" method.

## 
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
## 
## Adjustment was applied using the 'mean' method.
## 
## Rank 1 of 180 adjusted rate(s):
## Rate          : -0.08914513
## Adjustment    : 0.0001987835
## Adjusted Rate : -0.08934391 
## 
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------

Converting units

mmr_adj2 <- mmr_adj |> 
  convert_rate(oxy.unit = "%Air", 
               time.unit = "secs", 
               output.unit = "mg/h/kg", 
               volume = chamber_vol,
               mass = mass,
               S = salinity, 
               t = as.numeric(unique(firesting2$temperature)), 
               P = 1.013253)

## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.

selecting rates

mmr_final <- mmr_adj2 |> 
  select_rate(method = "rsq", n=c(0.89,1)) |> 
  select_rate(method = "highest", n=1) |> 
  plot(type="full") |> 
  summary(export=TRUE)

## select_rate: Selecting rates with rsq values between 0.89 and 1...

## ----- Selection complete. 35 rate(s) removed, 145 rate(s) remaining -----

## select_rate: Selecting highest 1 *absolute* rate values...

## ----- Selection complete. 144 rate(s) removed, 1 rate(s) remaining -----

## 
## # plot.convert_rate # -------------------

## plot.convert_rate: Plotting all rate(s)...

## -----------------------------------------
## 
## # summary.convert_rate # ----------------
## Summary of all converted rates:
## 
##    rep rank intercept_b0    slope_b1       rsq density row endrow    time
## 1:  NA    1     644.7807 -0.08914513 0.9955127      NA  38     83 6159.56
##    endtime    oxy endoxy        rate   adjustment rate.adjusted  rate.input
## 1: 6219.56 95.615  90.38 -0.08914513 0.0001987835   -0.08934391 -0.08934391
##    oxy.unit time.unit  volume      mass area  S    t        P   rate.abs
## 1:     %Air       sec 0.04573 0.0008797   NA 35 28.5 1.013253 -0.9369498
##    rate.m.spec rate.a.spec output.unit rate.output
## 1:   -1065.079          NA  mgO2/hr/kg   -1065.079
## -----------------------------------------

Results

results <-  results |> 
  mutate(Max_kg = mmr_final$rate.output*-1, 
         Max = (mmr_final$rate.output*-1)*mass,
         rsqmax =mmr_final$rsq,
         AAS_kg = Max_kg - Resting_kg, 
         AAS = Max - Resting, 
         Notes=Notes, 
         True_resting="") 
knitr::kable(results, "simple") 

Clutch	Replicate	Male	Female	Population	Tank	Mass	Chamber	System	Volume	Date_tested	Date_analysed	Swim	Salinity	Temperature	Resting_kg	Resting	rsqrest	Max_kg	Max	rsqmax	AAS_kg	AAS	Notes	True_resting
133	8	CARL335	CARL59	Arlington reef	139	0.0008797	ch1	Asus	0.04573	2023-07-01	2024-06-07	good/good	35	28.5	353.4737	0.3109508	0.9934	1065.079	0.9369498	0.9955127	711.6052	0.6259991	Chamber2 was accidentally in the position of chamber1
### Expor	ting data

resp_results_juveniles <- read_csv("resp_results_juveniles.csv") 

## Rows: 17 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

resp_results_juveniles <- rbind(resp_results_juveniles, results) 
resp_results_juveniles 

write.csv(resp_results_juveniles, file="./resp_results_juveniles.csv", row.names = FALSE)